{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from linearmodels.panel import compare\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from linearmodels.panel import PanelOLS, RandomEffects\n",
    "from scipy.stats import chi2\n",
    "from statsmodels.stats.diagnostic import het_breuschpagan\n",
    "from statsmodels.stats.stattools import durbin_watson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "file_path = '../data/CA2_filled.csv'\n",
    "data = pd.read_csv(file_path)\n",
    "\n",
    "# Data preprocessing: dependent and independent variables\n",
    "# Dependent variable: log(GDP)\n",
    "# Independent variables: edu_m, edu_f, gra_m, gra_f, gee, er_m, er_f, law, cpi\n",
    "\n",
    "# Convert variables to appropriate data types (ensure correctness)\n",
    "data['Country'] = data['Country'].astype('category')\n",
    "data['Year'] = data['Year'].astype('int')\n",
    "\n",
    "# Apply log transformation to gdp\n",
    "data['log_gdp'] = np.log(data['gdp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['gpi'] = data['edu_f'] / data['edu_m']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multicollinearity check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Variance Inflation Factor (VIF) before removing variables:\n",
      "    Variable          VIF\n",
      "0    log_gdp     4.394076\n",
      "1        gpi     2.323561\n",
      "2        gee     2.752525\n",
      "3       er_m     5.068563\n",
      "4       er_f     2.039909\n",
      "5        law     2.791146\n",
      "6        cpi     1.563645\n",
      "7  Intercept  1108.911946\n"
     ]
    }
   ],
   "source": [
    "# Multicollinearity check using Variance Inflation Factor (VIF)\n",
    "print(\"\\nVariance Inflation Factor (VIF) before removing variables:\")\n",
    "X = data[['log_gdp', 'gpi', 'gee', 'er_m', 'er_f', 'law', 'cpi']].copy()\n",
    "X.loc[:, 'Intercept'] = 1  # Add intercept for VIF calculation\n",
    "vif_data_before = pd.DataFrame()\n",
    "vif_data_before['Variable'] = X.columns\n",
    "vif_data_before['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "print(vif_data_before)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# hausman test ---choose FE model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Hausman Test:\n",
      "Hausman Statistic: 82.97849316166014\n",
      "P-value: 3.4416913763379853e-15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:599: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:601: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:599: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:601: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:685: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:687: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:685: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:687: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n"
     ]
    }
   ],
   "source": [
    "# Hausman test for fixed vs. random effects\n",
    "# Using linearmodels library for panel data regression\n",
    "from linearmodels.panel import compare\n",
    "\n",
    "# Fixed effects model\n",
    "fixed_effects = PanelOLS.from_formula('edu ~ log_gdp + gpi + gee + er_m + er_f + law + cpi + EntityEffects', data=data.set_index(['Country', 'Year'])).fit()\n",
    "\n",
    "# Random effects model\n",
    "random_effects = RandomEffects.from_formula('edu ~ log_gdp + gpi + gee + er_m + er_f + law + cpi', data=data.set_index(['Country', 'Year'])).fit()\n",
    "\n",
    "# Standard Hausman test\n",
    "print(\"\\nHausman Test:\")\n",
    "fe_params = fixed_effects.params\n",
    "re_params = random_effects.params\n",
    "common_params = fe_params.index.intersection(re_params.index)\n",
    "\n",
    "b_diff = fe_params[common_params] - re_params[common_params]\n",
    "V_fe = fixed_effects.cov.loc[common_params, common_params]\n",
    "V_re = random_effects.cov.loc[common_params, common_params]\n",
    "V_diff = V_fe - V_re\n",
    "\n",
    "hausman_stat = b_diff.T @ np.linalg.inv(V_diff) @ b_diff\n",
    "p_value = 1 - chi2.cdf(hausman_stat, len(common_params))\n",
    "\n",
    "print(f\"Hausman Statistic: {hausman_stat}\")\n",
    "print(f\"P-value: {p_value}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Education Level over time for each country using seaborn\n",
    "plt.figure(figsize=(12, 6))\n",
    "for country in data['Country'].unique():\n",
    "    country_data = data[data['Country'] == country]\n",
    "    plt.plot(country_data['Year'], country_data['edu'], label=country)\n",
    "\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Edu level')\n",
    "plt.title('Education Level Over Time by Country')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# without interactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fixed Effects Regression Results:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    edu   R-squared:                        0.5759\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -15.277\n",
      "No. Observations:                 220   R-squared (Within):               0.5759\n",
      "Date:                Fri, Oct 25 2024   R-squared (Overall):             -14.552\n",
      "Time:                        00:53:11   Log-likelihood                   -629.31\n",
      "Cov. Estimator:                Robust                                           \n",
      "                                        F-statistic:                      39.182\n",
      "Entities:                          11   P-value                           0.0000\n",
      "Avg Obs:                       20.000   Distribution:                   F(7,202)\n",
      "Min Obs:                       20.000                                           \n",
      "Max Obs:                       20.000   F-statistic (robust):             28.679\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                      20   Distribution:                   F(7,202)\n",
      "Avg Obs:                       11.000                                           \n",
      "Min Obs:                       11.000                                           \n",
      "Max Obs:                       11.000                                           \n",
      "                                                                                \n",
      "                             Parameter Estimates                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "log_gdp        13.324     1.7886     7.4495     0.0000      9.7974      16.851\n",
      "gpi           -18.652     6.7454    -2.7652     0.0062     -31.953     -5.3518\n",
      "gee           -0.1684     0.5841    -0.2883     0.7734     -1.3201      0.9833\n",
      "er_m          -0.6604     0.3120    -2.1168     0.0355     -1.2755     -0.0453\n",
      "er_f           1.4766     0.2015     7.3267     0.0000      1.0792      1.8740\n",
      "law            11.001     1.9302     5.6991     0.0000      7.1947      14.807\n",
      "cpi            0.1980     0.1561     1.2683     0.2061     -0.1098      0.5059\n",
      "==============================================================================\n",
      "\n",
      "F-test for Poolability: 22.392\n",
      "P-value: 0.0000\n",
      "Distribution: F(10,202)\n",
      "\n",
      "Included effects: Entity\n",
      "\n",
      "Fixed Effects Regression Results (Male) with Robust Standard Errors:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    edu   R-squared:                        0.4737\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -46.099\n",
      "No. Observations:                 220   R-squared (Within):               0.4737\n",
      "Date:                Fri, Oct 25 2024   R-squared (Overall):             -43.970\n",
      "Time:                        00:53:11   Log-likelihood                   -653.05\n",
      "Cov. Estimator:                Robust                                           \n",
      "                                        F-statistic:                      30.456\n",
      "Entities:                          11   P-value                           0.0000\n",
      "Avg Obs:                       20.000   Distribution:                   F(6,203)\n",
      "Min Obs:                       20.000                                           \n",
      "Max Obs:                       20.000   F-statistic (robust):             16.536\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                      20   Distribution:                   F(6,203)\n",
      "Avg Obs:                       11.000                                           \n",
      "Min Obs:                       11.000                                           \n",
      "Max Obs:                       11.000                                           \n",
      "                                                                                \n",
      "                             Parameter Estimates                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "log_gdp        17.583     2.0481     8.5848     0.0000      13.544      21.621\n",
      "gpi           -7.2291     6.5074    -1.1109     0.2679     -20.060      5.6017\n",
      "gee           -0.0396     0.6040    -0.0656     0.9477     -1.2305      1.1512\n",
      "er_m           0.9190     0.2339     3.9281     0.0001      0.4577      1.3802\n",
      "law            11.353     1.9247     5.8987     0.0000      7.5585      15.149\n",
      "cpi            0.1375     0.1415     0.9718     0.3323     -0.1415      0.4165\n",
      "==============================================================================\n",
      "\n",
      "F-test for Poolability: 14.452\n",
      "P-value: 0.0000\n",
      "Distribution: F(10,203)\n",
      "\n",
      "Included effects: Entity\n",
      "\n",
      "Fixed Effects Regression Results (Female) with Robust Standard Errors:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    edu   R-squared:                        0.5668\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -30.648\n",
      "No. Observations:                 220   R-squared (Within):               0.5668\n",
      "Date:                Fri, Oct 25 2024   R-squared (Overall):             -29.221\n",
      "Time:                        00:53:12   Log-likelihood                   -631.63\n",
      "Cov. Estimator:                Robust                                           \n",
      "                                        F-statistic:                      44.273\n",
      "Entities:                          11   P-value                           0.0000\n",
      "Avg Obs:                       20.000   Distribution:                   F(6,203)\n",
      "Min Obs:                       20.000                                           \n",
      "Max Obs:                       20.000   F-statistic (robust):             27.913\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                      20   Distribution:                   F(6,203)\n",
      "Avg Obs:                       11.000                                           \n",
      "Min Obs:                       11.000                                           \n",
      "Max Obs:                       11.000                                           \n",
      "                                                                                \n",
      "                             Parameter Estimates                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "log_gdp        14.611     1.6422     8.8975     0.0000      11.373      17.849\n",
      "gpi           -14.247     6.1687    -2.3096     0.0219     -26.410     -2.0841\n",
      "gee            0.1165     0.5840     0.1995     0.8420     -1.0350      1.2681\n",
      "er_f           1.1638     0.1602     7.2644     0.0000      0.8479      1.4797\n",
      "law            11.949     1.7701     6.7504     0.0000      8.4585      15.439\n",
      "cpi            0.1855     0.1474     1.2590     0.2095     -0.1050      0.4761\n",
      "==============================================================================\n",
      "\n",
      "F-test for Poolability: 21.609\n",
      "P-value: 0.0000\n",
      "Distribution: F(10,203)\n",
      "\n",
      "Included effects: Entity\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n"
     ]
    }
   ],
   "source": [
    "# Fixed effects regression\n",
    "fe_model = PanelOLS.from_formula('edu ~ log_gdp + gpi +gee + er_m + er_f + law + cpi + EntityEffects', \n",
    "                                 data=data.set_index(['Country', 'Year'])).fit(cov_type='robust')\n",
    "# Output the regression result\n",
    "print(\"\\nFixed Effects Regression Results:\")\n",
    "print(fe_model.summary)\n",
    "\n",
    "# Fixed effects regression for male data only with robust standard errors\n",
    "fe_model_male = PanelOLS.from_formula('edu ~ log_gdp + gpi + gee + er_m + law + cpi + EntityEffects', data=data.set_index(['Country', 'Year'])).fit(cov_type='robust')\n",
    "# Output the regression result for male data\n",
    "print(\"\\nFixed Effects Regression Results (Male) with Robust Standard Errors:\")\n",
    "print(fe_model_male.summary)\n",
    "\n",
    "# Fixed effects regression for female data only with robust standard errors\n",
    "fe_model_female = PanelOLS.from_formula('edu ~ log_gdp + gpi + gee + er_f + law + cpi + EntityEffects', data=data.set_index(['Country', 'Year'])).fit(cov_type='robust')\n",
    "# Output the regression result for female data\n",
    "print(\"\\nFixed Effects Regression Results (Female) with Robust Standard Errors:\")\n",
    "print(fe_model_female.summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# with interactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create interaction terms\n",
    "data['law_er_m'] = data['law'] * data['er_m']\n",
    "data['law_er_f'] = data['law'] * data['er_f']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fixed Effects Regression Results with Interaction Terms:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    edu   R-squared:                        0.5968\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -9.4545\n",
      "No. Observations:                 220   R-squared (Within):               0.5968\n",
      "Date:                Fri, Oct 25 2024   R-squared (Overall):             -8.9950\n",
      "Time:                        00:53:12   Log-likelihood                   -623.73\n",
      "Cov. Estimator:                Robust                                           \n",
      "                                        F-statistic:                      32.899\n",
      "Entities:                          11   P-value                           0.0000\n",
      "Avg Obs:                       20.000   Distribution:                   F(9,200)\n",
      "Min Obs:                       20.000                                           \n",
      "Max Obs:                       20.000   F-statistic (robust):             26.133\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                      20   Distribution:                   F(9,200)\n",
      "Avg Obs:                       11.000                                           \n",
      "Min Obs:                       11.000                                           \n",
      "Max Obs:                       11.000                                           \n",
      "                                                                                \n",
      "                             Parameter Estimates                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "log_gdp        12.208     1.9568     6.2384     0.0000      8.3489      16.066\n",
      "gpi           -16.024     6.8906    -2.3256     0.0210     -29.612     -2.4370\n",
      "gee           -0.4396     0.5778    -0.7608     0.4477     -1.5789      0.6997\n",
      "er_m          -0.7761     0.2781    -2.7904     0.0058     -1.3245     -0.2276\n",
      "er_f           1.3688     0.2113     6.4786     0.0000      0.9522      1.7855\n",
      "law           -12.937     18.650    -0.6937     0.4887     -49.714      23.840\n",
      "cpi            0.1952     0.1583     1.2336     0.2188     -0.1168      0.5073\n",
      "law_er_m      -1.2333     0.7141    -1.7271     0.0857     -2.6414      0.1748\n",
      "law_er_f       1.7862     0.7558     2.3633     0.0191      0.2958      3.2766\n",
      "==============================================================================\n",
      "\n",
      "F-test for Poolability: 23.054\n",
      "P-value: 0.0000\n",
      "Distribution: F(10,200)\n",
      "\n",
      "Included effects: Entity\n",
      "\n",
      "Fixed Effects Regression Results (Male) with Interaction Term and Robust Standard Errors:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    edu   R-squared:                        0.4737\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -46.498\n",
      "No. Observations:                 220   R-squared (Within):               0.4737\n",
      "Date:                Fri, Oct 25 2024   R-squared (Overall):             -44.350\n",
      "Time:                        00:53:12   Log-likelihood                   -653.05\n",
      "Cov. Estimator:                Robust                                           \n",
      "                                        F-statistic:                      25.977\n",
      "Entities:                          11   P-value                           0.0000\n",
      "Avg Obs:                       20.000   Distribution:                   F(7,202)\n",
      "Min Obs:                       20.000                                           \n",
      "Max Obs:                       20.000   F-statistic (robust):             17.092\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                      20   Distribution:                   F(7,202)\n",
      "Avg Obs:                       11.000                                           \n",
      "Min Obs:                       11.000                                           \n",
      "Max Obs:                       11.000                                           \n",
      "                                                                                \n",
      "                             Parameter Estimates                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "log_gdp        17.625     2.3027     7.6538     0.0000      13.084      22.165\n",
      "gpi           -7.2991     7.1723    -1.0177     0.3100     -21.441      6.8430\n",
      "gee           -0.0420     0.6104    -0.0688     0.9452     -1.2457      1.1617\n",
      "er_m           0.9263     0.2553     3.6287     0.0004      0.4230      1.4297\n",
      "law            12.178     18.604     0.6546     0.5135     -24.505      48.860\n",
      "cpi            0.1359     0.1507     0.9017     0.3683     -0.1612      0.4330\n",
      "law_er_m      -0.0117     0.2784    -0.0419     0.9666     -0.5607      0.5373\n",
      "==============================================================================\n",
      "\n",
      "F-test for Poolability: 14.082\n",
      "P-value: 0.0000\n",
      "Distribution: F(10,202)\n",
      "\n",
      "Included effects: Entity\n",
      "\n",
      "Fixed Effects Regression Results (Female) with Interaction Term and Robust Standard Errors:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    edu   R-squared:                        0.5707\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -27.663\n",
      "No. Observations:                 220   R-squared (Within):               0.5707\n",
      "Date:                Fri, Oct 25 2024   R-squared (Overall):             -26.372\n",
      "Time:                        00:53:12   Log-likelihood                   -630.64\n",
      "Cov. Estimator:                Robust                                           \n",
      "                                        F-statistic:                      38.364\n",
      "Entities:                          11   P-value                           0.0000\n",
      "Avg Obs:                       20.000   Distribution:                   F(7,202)\n",
      "Min Obs:                       20.000                                           \n",
      "Max Obs:                       20.000   F-statistic (robust):             25.052\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                      20   Distribution:                   F(7,202)\n",
      "Avg Obs:                       11.000                                           \n",
      "Min Obs:                       11.000                                           \n",
      "Max Obs:                       11.000                                           \n",
      "                                                                                \n",
      "                             Parameter Estimates                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "log_gdp        13.899     1.8632     7.4598     0.0000      10.225      17.573\n",
      "gpi           -11.422     7.0031    -1.6311     0.1044     -25.231      2.3860\n",
      "gee            0.1978     0.6029     0.3281     0.7432     -0.9910      1.3866\n",
      "er_f           1.0909     0.1667     6.5459     0.0000      0.7623      1.4195\n",
      "law           -7.5673     15.456    -0.4896     0.6249     -38.043      22.908\n",
      "cpi            0.2140     0.1470     1.4560     0.1469     -0.0758      0.5039\n",
      "law_er_f       0.3186     0.2671     1.1928     0.2343     -0.2081      0.8453\n",
      "==============================================================================\n",
      "\n",
      "F-test for Poolability: 21.478\n",
      "P-value: 0.0000\n",
      "Distribution: F(10,202)\n",
      "\n",
      "Included effects: Entity\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n"
     ]
    }
   ],
   "source": [
    "# Fixed effects regression with interaction terms\n",
    "fe_model1 = PanelOLS.from_formula('edu ~ log_gdp + gpi + gee + er_m + er_f + law + cpi + law_er_m + law_er_f + EntityEffects',\n",
    "                                 data=data.set_index(['Country', 'Year'])).fit(cov_type='robust')\n",
    "# Output the regression result\n",
    "print(\"\\nFixed Effects Regression Results with Interaction Terms:\")\n",
    "print(fe_model1.summary)\n",
    "\n",
    "# Fixed effects regression for male data only with interaction term and robust standard errors\n",
    "fe_model_male1 = PanelOLS.from_formula('edu ~ log_gdp + gpi + gee + er_m + law + cpi + law_er_m + EntityEffects', data=data.set_index(['Country', 'Year'])).fit(cov_type='robust')\n",
    "# Output the regression result for male data\n",
    "print(\"\\nFixed Effects Regression Results (Male) with Interaction Term and Robust Standard Errors:\")\n",
    "print(fe_model_male1.summary)\n",
    "\n",
    "# Fixed effects regression for female data only with interaction term and robust standard errors\n",
    "fe_model_female1 = PanelOLS.from_formula('edu ~ log_gdp + gpi + gee + er_f + law + cpi + law_er_f + EntityEffects', data=data.set_index(['Country', 'Year'])).fit(cov_type='robust')\n",
    "# Output the regression result for female data\n",
    "print(\"\\nFixed Effects Regression Results (Female) with Interaction Term and Robust Standard Errors:\")\n",
    "print(fe_model_female1.summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Robustness check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fixed Effects Regression Results with Interaction Terms and kernel Standard Errors:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    edu   R-squared:                        0.5968\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -9.4545\n",
      "No. Observations:                 220   R-squared (Within):               0.5968\n",
      "Date:                Fri, Oct 25 2024   R-squared (Overall):             -8.9950\n",
      "Time:                        00:54:19   Log-likelihood                   -623.73\n",
      "Cov. Estimator:        Driscoll-Kraay                                           \n",
      "                                        F-statistic:                      32.899\n",
      "Entities:                          11   P-value                           0.0000\n",
      "Avg Obs:                       20.000   Distribution:                   F(9,200)\n",
      "Min Obs:                       20.000                                           \n",
      "Max Obs:                       20.000   F-statistic (robust):             422.70\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                      20   Distribution:                   F(9,200)\n",
      "Avg Obs:                       11.000                                           \n",
      "Min Obs:                       11.000                                           \n",
      "Max Obs:                       11.000                                           \n",
      "                                                                                \n",
      "                             Parameter Estimates                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "log_gdp        12.208     2.1956     5.5599     0.0000      7.8780      16.537\n",
      "gpi           -16.024     7.1278    -2.2482     0.0257     -30.080     -1.9693\n",
      "gee           -0.4396     0.4365    -1.0071     0.3151     -1.3003      0.4211\n",
      "er_m          -0.7761     0.3559    -2.1803     0.0304     -1.4779     -0.0742\n",
      "er_f           1.3688     0.2726     5.0217     0.0000      0.8313      1.9064\n",
      "law           -12.937     19.923    -0.6493     0.5169     -52.224      26.350\n",
      "cpi            0.1952     0.2007     0.9728     0.3318     -0.2005      0.5909\n",
      "law_er_m      -1.2333     0.8196    -1.5048     0.1340     -2.8494      0.3828\n",
      "law_er_f       1.7862     0.9555     1.8695     0.0630     -0.0978      3.6703\n",
      "==============================================================================\n",
      "\n",
      "F-test for Poolability: 23.054\n",
      "P-value: 0.0000\n",
      "Distribution: F(10,200)\n",
      "\n",
      "Included effects: Entity\n",
      "\n",
      "Fixed Effects Regression Results (Male) with Interaction Term and kernel Standard Errors:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    edu   R-squared:                        0.4737\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -46.498\n",
      "No. Observations:                 220   R-squared (Within):               0.4737\n",
      "Date:                Fri, Oct 25 2024   R-squared (Overall):             -44.350\n",
      "Time:                        00:54:19   Log-likelihood                   -653.05\n",
      "Cov. Estimator:        Driscoll-Kraay                                           \n",
      "                                        F-statistic:                      25.977\n",
      "Entities:                          11   P-value                           0.0000\n",
      "Avg Obs:                       20.000   Distribution:                   F(7,202)\n",
      "Min Obs:                       20.000                                           \n",
      "Max Obs:                       20.000   F-statistic (robust):             365.06\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                      20   Distribution:                   F(7,202)\n",
      "Avg Obs:                       11.000                                           \n",
      "Min Obs:                       11.000                                           \n",
      "Max Obs:                       11.000                                           \n",
      "                                                                                \n",
      "                             Parameter Estimates                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "log_gdp        17.625     2.4217     7.2777     0.0000      12.850      22.400\n",
      "gpi           -7.2991     8.1194    -0.8990     0.3697     -23.309      8.7106\n",
      "gee           -0.0420     0.5179    -0.0811     0.9354     -1.0631      0.9791\n",
      "er_m           0.9263     0.2061     4.4945     0.0000      0.5199      1.3327\n",
      "law            12.178     20.029     0.6080     0.5439     -27.315      51.671\n",
      "cpi            0.1359     0.2393     0.5678     0.5708     -0.3360      0.6077\n",
      "law_er_m      -0.0117     0.2922    -0.0400     0.9682     -0.5878      0.5644\n",
      "==============================================================================\n",
      "\n",
      "F-test for Poolability: 14.082\n",
      "P-value: 0.0000\n",
      "Distribution: F(10,202)\n",
      "\n",
      "Included effects: Entity\n",
      "\n",
      "Fixed Effects Regression Results (Female) with Interaction Term and kernel Standard Errors:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    edu   R-squared:                        0.5707\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -27.663\n",
      "No. Observations:                 220   R-squared (Within):               0.5707\n",
      "Date:                Fri, Oct 25 2024   R-squared (Overall):             -26.372\n",
      "Time:                        00:54:19   Log-likelihood                   -630.64\n",
      "Cov. Estimator:        Driscoll-Kraay                                           \n",
      "                                        F-statistic:                      38.364\n",
      "Entities:                          11   P-value                           0.0000\n",
      "Avg Obs:                       20.000   Distribution:                   F(7,202)\n",
      "Min Obs:                       20.000                                           \n",
      "Max Obs:                       20.000   F-statistic (robust):             882.34\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                      20   Distribution:                   F(7,202)\n",
      "Avg Obs:                       11.000                                           \n",
      "Min Obs:                       11.000                                           \n",
      "Max Obs:                       11.000                                           \n",
      "                                                                                \n",
      "                             Parameter Estimates                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "log_gdp        13.899     1.8616     7.4661     0.0000      10.228      17.570\n",
      "gpi           -11.422     8.0723    -1.4150     0.1586     -27.339      4.4943\n",
      "gee            0.1978     0.6468     0.3059     0.7600     -1.0775      1.4731\n",
      "er_f           1.0909     0.1681     6.4879     0.0000      0.7593      1.4224\n",
      "law           -7.5673     17.580    -0.4305     0.6673     -42.230      27.096\n",
      "cpi            0.2140     0.1981     1.0804     0.2812     -0.1766      0.6047\n",
      "law_er_f       0.3186     0.2973     1.0717     0.2851     -0.2676      0.9049\n",
      "==============================================================================\n",
      "\n",
      "F-test for Poolability: 21.478\n",
      "P-value: 0.0000\n",
      "Distribution: F(10,202)\n",
      "\n",
      "Included effects: Entity\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n"
     ]
    }
   ],
   "source": [
    "# Fixed effects regression with interaction terms\n",
    "fe_model = PanelOLS.from_formula('edu ~ log_gdp + gpi + gee + er_m + er_f + law + cpi + law_er_m + law_er_f + EntityEffects', \n",
    "                                 data=data.set_index(['Country', 'Year'])).fit(cov_type='kernel')\n",
    "# Output the regression result\n",
    "print(\"\\nFixed Effects Regression Results with Interaction Terms and kernel Standard Errors:\")\n",
    "print(fe_model.summary)\n",
    "\n",
    "# Fixed effects regression for male data only with interaction term and kernel standard errors\n",
    "fe_model_male = PanelOLS.from_formula('edu ~ log_gdp + gpi + gee + er_m + law + cpi + law_er_m + EntityEffects', data=data.set_index(['Country', 'Year'])).fit(cov_type='kernel')\n",
    "# Output the regression result for male data\n",
    "print(\"\\nFixed Effects Regression Results (Male) with Interaction Term and kernel Standard Errors:\")\n",
    "print(fe_model_male.summary)\n",
    "\n",
    "# Fixed effects regression for female data only with interaction term and kernel standard errors\n",
    "fe_model_female = PanelOLS.from_formula('edu ~ log_gdp + gpi + gee + er_f + law + cpi + law_er_f + EntityEffects', data=data.set_index(['Country', 'Year'])).fit(cov_type='kernel')\n",
    "# Output the regression result for female data\n",
    "print(\"\\nFixed Effects Regression Results (Female) with Interaction Term and kernel Standard Errors:\")\n",
    "print(fe_model_female.summary)"
   ]
  }
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